Abstract
Production planning department prepares demand for the coming months considering the plant capacity, available time. Depending on this demand, inventory of the raw material is kept in the production unit. While, in the scheduling problem, the time horizon is selected as a shift, day or week. Scheduling model determines the start time, processing time, finish time and transition time. However, in most of the reported literature, production planning problem and scheduling problem are solved independently. But, to achieve the global optimum solution and minimise material flow and reduce the total cost, there is a need of integrating production planning and scheduling model. A case study based on the parallel line continuous process plant is selected and optimisation is obtained by real coded genetic algorithm (RCGA). Results show that RCGA outperforms the solutions obtained by previous researchers.
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References
Chu, Y., et al.: Integrated planning and scheduling under production uncertainties: Bi-level model formulation and hybrid solution method. Comput. Chem. Eng. 72, 255–272 (2015). https://doi.org/10.1016/j.compchemeng.2014.02.023
Kopanos, G.M., Puigjaner, L., Georgiadis, M.C.: Optimal production scheduling and lot-sizing in dairy plants: the yogurt production line. Ind. Eng. Chem. Res. 49(2), 701–718 (2010). https://doi.org/10.1021/ie901013k
Kopanos, G.M., Puigjaner, L., Maravelias, C.T.: Production planning and scheduling of parallel continuous processes with product families. Ind. Eng. Chem. Res 50, 1369–1378 (2011). https://doi.org/10.1021/ie100790t
Bhosale, K.C., Pawar, P.J.: Flex Serv Manuf J (2018). https://doi.org/10.1007/s10696-018-9310-5
Bhosale, K.C., Pawar, P.J.: Optimisation of operation allocation problem in flexible manufacturing system by real coded genetic algorithm. Mater. Today: Proc. 5, 7160–7167 (2018). https://doi.org/10.1016/j.matpr.2017.11.381
Herrera, F., Lozano, M., Verdegay, J.L.: Tackling real-coded genetic algorithms: operators and tools for behavioural analysis. Artif. Intell. Rev. 12(4), 265–319 (1998). https://doi.org/10.1023/A:1006504901164
Chuang, Y.-C., Chen, C.-T., Hwang, C.: A simple and efficient real-coded genetic algorithm for constrained optimization. Appl. Soft Comput. 38, 87–105 (2016). https://doi.org/10.1016/j.asoc.2015.09.036
Deb, K., Kumar, A.: Real-coded genetic algorithms with simulated binary crossover: studies on multimodal and multiobjective problems. Complex Syst. 9, 431–454 (1995)
Homaifar, A., Qi, C.X., Lai, S.H.: Constrained optimization via genetic algorithms. Simulation 62(4), 242–253 (1994)
Kumar, N., Shanker, K.: A genetic algorithm for FMS part type selection and machine loading. Int. J. Prod. Res. 38(16), 3861–3887 (2000). https://doi.org/10.1080/00207540050176058
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Bhosale, K.C., Pawar, P.J. (2020). Integrated Production Planning and Scheduling for Parallel Production Lines. In: Venkata Rao, R., Taler, J. (eds) Advanced Engineering Optimization Through Intelligent Techniques. Advances in Intelligent Systems and Computing, vol 949. Springer, Singapore. https://doi.org/10.1007/978-981-13-8196-6_59
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DOI: https://doi.org/10.1007/978-981-13-8196-6_59
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